benchmark-hillclimb
CommunityImprove AI systems with trace-driven benchmark experiments.
Software Engineering#AI optimization#AI debugging#benchmark testing#benchmark analysis#experimentation workflow
Authormick-net
Version1.0.0
Installs0
System Documentation
What problem does it solve?
The Benchmark Hillclimb skill helps improve AI agents, retrieval systems, or product workflows by guiding trace-driven benchmark experiments, allowing for evidence-based optimizations and debugging.
Core Features & Use Cases
- Benchmark Experimentation: Provides guidelines for creating focused, anti-overfitting experiments using benchmark tests.
- Behavior Class Analysis: Classifies failures into specific categories for targeted troubleshooting and improvements.
- Optimization Logging: Supports recording and tracking changes made during experiments, aiding in decision-making and future optimization.
- Research Integration: Includes research intake guidance to explore primary sources when basic fixes have failed.
Quick Start
Execute a benchmark-hillclimb run with a new experiment pack for performance optimization.
Dependency Matrix
Required Modules
None requiredComponents
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: benchmark-hillclimb Download link: https://github.com/mick-net/Skills/archive/main.zip#benchmark-hillclimb Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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